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        <title>Team Ohio State at CMCL 2021 Shared Task: Fine-Tuned RoBERTa for Eye-Tracking Data Prediction</title>
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            <namePart type="given">Nora</namePart>
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    <abstract>This paper describes Team Ohio State’s approach to the CMCL 2021 Shared Task, the goal of which is to predict five eye-tracking features from naturalistic self-paced reading corpora. For this task, we fine-tune a pre-trained neural language model (RoBERTa; Liu et al., 2019) to predict each feature based on the contextualized representations. Moreover, motivated by previous eye-tracking studies, we include word length in characters and proportion of sentence processed as two additional input features. Our best model strongly outperforms the baseline and is also competitive with other systems submitted to the shared task. An ablation study shows that the word length feature contributes to making more accurate predictions, indicating the usefulness of features that are specific to the eye-tracking paradigm.</abstract>
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            <start>97</start>
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%0 Conference Proceedings
%T Team Ohio State at CMCL 2021 Shared Task: Fine-Tuned RoBERTa for Eye-Tracking Data Prediction
%A Oh, Byung-Doh
%Y Chersoni, Emmanuele
%Y Hollenstein, Nora
%Y Jacobs, Cassandra
%Y Oseki, Yohei
%Y Prévot, Laurent
%Y Santus, Enrico
%S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F oh-2021-team
%X This paper describes Team Ohio State’s approach to the CMCL 2021 Shared Task, the goal of which is to predict five eye-tracking features from naturalistic self-paced reading corpora. For this task, we fine-tune a pre-trained neural language model (RoBERTa; Liu et al., 2019) to predict each feature based on the contextualized representations. Moreover, motivated by previous eye-tracking studies, we include word length in characters and proportion of sentence processed as two additional input features. Our best model strongly outperforms the baseline and is also competitive with other systems submitted to the shared task. An ablation study shows that the word length feature contributes to making more accurate predictions, indicating the usefulness of features that are specific to the eye-tracking paradigm.
%R 10.18653/v1/2021.cmcl-1.11
%U https://aclanthology.org/2021.cmcl-1.11
%U https://doi.org/10.18653/v1/2021.cmcl-1.11
%P 97-101

Markdown (Informal)

[Team Ohio State at CMCL 2021 Shared Task: Fine-Tuned RoBERTa for Eye-Tracking Data Prediction](https://aclanthology.org/2021.cmcl-1.11) (Oh, CMCL 2021)

• Team Ohio State at CMCL 2021 Shared Task: Fine-Tuned RoBERTa for Eye-Tracking Data Prediction (Oh, CMCL 2021)

ACL

• Byung-Doh Oh. 2021. Team Ohio State at CMCL 2021 Shared Task: Fine-Tuned RoBERTa for Eye-Tracking Data Prediction. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 97–101, Online. Association for Computational Linguistics.